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• Journal article
  Markus JE, Cristinacce PLH, Punwani S, O'Connor JPB, Mills R, Lopez MY, Grech-Sollars M, Fasano F, Waterton JC, Thrippleton MJ, Hall MG, Francis ST, Statton B, Murphy K, So P-W, Hyare Het al., 2025,

  Steps on the Path to Clinical Translation-A British and Irish Chapter ISMRM Workshop Survey of the UK MRI Community

  , MAGNETIC RESONANCE IN MEDICINE, ISSN: 0740-3194
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• Journal article
  Khalique Z, Scott AD, Ferreira PF, Molto M, Nielles-Vallespin S, Pennell DJet al., 2025,

  Diffusion Tensor CMR Assessment of the Microstructural Response to Dobutamine Stress in Health and Comparison With Patients With Recovered Dilated Cardiomyopathy.

  , Circ Cardiovasc Imaging

  BACKGROUND: Contractile reserve assessment assesses myocardial performance and prognosis. The microstructural mechanisms that facilitate increased cardiac function have not been described, but can be studied using diffusion tensor cardiovascular magnetic resonance. Resting microstructural contractile function is characterized by reorientation of aggregated cardiomyocytes (sheetlets) from wall-parallel in diastole to a more wall-perpendicular configuration in systole, with the diffusion tensor cardiovascular magnetic resonance parameter E2A defining their orientation, and sheetlet mobility defining the angle through which they rotate. We used diffusion tensor cardiovascular magnetic resonance to identify the microstructural response to dobutamine stress in healthy volunteers and then compared with patients with recovered dilated cardiomyopathy (rDCM). METHODS: In this first-of-its-kind prospective observational study, 20 healthy volunteers and 32 patients with rDCM underwent diffusion tensor cardiovascular magnetic resonance at rest, during dobutamine, and on recovery. RESULTS: In healthy volunteers, both diastolic and systolic E2A increased with dobutamine stress (13±3° to 17±5°; P<0.001 and 59±11° to 65±7°; P=0.002). Sheetlet mobility remained unchanged (45±11° to 49±10°; P=0.19), but biphasic mean E2A increased (36±6° to 41±4°; P<0.001). In rDCM, diastolic E2A at rest was higher than in healthy volunteers (20±8° versus 13±3°, P<0.001), and sheetlet mobility was reduced (34±12° versus 45±11°; P<0.001). During dobutamine stress, rDCM diastolic and systolic E2A increased compared with rest (20±8° to 24±10°; P=0.001 and 54±13° to 63±11°; P=0.005). However, sheetlet mobility in patients with rDCM failed to increase with dobutamine to healthy levels (39±13° versus 49±

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• Journal article
  Tänzer M, Scott AD, Khalique Z, Molto M, Rajakulasingam R, Silva RD, Pennell DJ, Ferreira PF, Yang G, Rueckert D, Nielles-Vallespin Set al., 2025,

  Accelerating cDTI with deep learning-based tensor de-noising and breath hold reduction. a step towards improved efficiency and clinical feasibility

  , Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647

  BackgroundCardiac Diffusion Tensor Imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.MethodsWe present a novel de-noising framework for cDTI acceleration centred on three fundamental advances: (1) a paradigm shift from image-based to tensor-space de-noising that better preserves structural information, (2) an ensemble of Vision Transformer-based models specifically optimised for tensor processing through adversarial training, and (3) a sophisticated data augmentation strategy that maximises training data utilisation through dynamic repetition selection.ResultsOur approach reduces scan times by a factor of up to 4 while achieving a 20% reduction in cDTI maps errors over existing de-noising methods (Table 1) and preserving anatomical features such as infarct characterisation and transmural cardiomyocyte orientation patterns. Crucially, our proposed method succeeds in clinical cases where other algorithms previously failed.ConclusionsThis demonstrates substantial improvements in cDTI acquisition efficiency, achieving up to 4-fold scan time reduction (3-5 breath-holds) while maintaining diagnostic accuracy across diverse cardiac pathologies.

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• Journal article
  Wang F, Wang Z, Li Y, Lyu J, Qin C, Wang S, Guo K, Sun M, Huang M, Zhang H, Tanzer M, Li Q, Chen X, Huang J, Wu Y, Zhang H, Hamedani KA, Lyu Y, Sun L, Li Q, He T, Lan L, Yao Q, Xu Z, Xin B, Metaxas DN, Razizadeh N, Nabavi S, Yiasemis G, Teuwen J, Zhang Z, Wang S, Zhang C, Ennis DB, Xue Z, Hu C, Xu R, Oksuz I, Lyu D, Huang Y, Guo X, Hao R, Patel JH, Cai G, Chen B, Zhang Y, Hua S, Chen Z, Dou Q, Zhuang X, Tao Q, Bai W, Qin J, Wang H, Prieto C, Markl M, Young A, Li H, Hu X, Wu L, Qu X, Yang G, Wang Cet al., 2025,

  Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.

  , IEEE Trans Med Imaging, Vol: PP

  Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the clinical reference standard for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging sequences, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen modalities and robustness to diverse undersampling patterns. We introduced the largest public multi-modality CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

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• Journal article
  Teh I, Moulin K, Ferreira PF, Absil J, Afzali M, Agger P, Akbari B, Aletras AH, Aono S, Benton C, Bhattacharya S, Croisille P, De Bruecker Y, Dall'Armellina E, Ennis DB, Glessgen C, Glinska A, Haltmeier S, Hannum A, Hedström E, Hussein T, Jones S, Joy G, Kettless K, Kim WY, Kozerke S, Magat J, Muthupillai R, Nezafat R, Nielles-Vallespin S, Oshinski J, Ozenne V, Pennell DJ, Pettigrew R, Pierce I, Raman B, Sabisz A, Schneider JE, Sherman JH, Shetye A, Symons R, Thoma P, Treibel T, Tsuneta S, Vallee J-P, Vejlstrup N, Viallon M, Nguyen C, Scott AD, Stoeck CTet al., 2025,

  Multi-center investigation of cardiac diffusion tensor imaging in healthy volunteers by the Society of Cardiovascular Magnetic Resonance Cardiac Diffusion Special Interest Group NETwork (SIGNET)

  , Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647

  BackgroundCardiac diffusion tensor imaging (cDTI) is an emerging technique for microstructural characterization of the heart and has shown clinical potential in a range of cardiomyopathies. However, there is substantial variation reported for in vivo cDTI results across the literature, and sensitivity of cDTI to differences in imaging sites, scanners, acquisition protocols, and post-processing methods remains incompletely understood.MethodsSIGNET is a prospective multi-center, observational study in traveling and non-traveling healthy volunteers. The study was initiated by the executive board of the Society of Cardiovascular Magnetic Resonance (SCMR) Cardiac Diffusion Special Interest Group (SIG) as a follow-up to a previous multi-center study on phantom validation of cardiac DTI and a recently published SCMR consensus statement on cardiac diffusion MRI. The study has been developed by the Project Management Committee in consultation with the SCMR cardiac diffusion SIG, which includes international experts in cardiac diffusion MRI. To date, more than 20 international institutions have engaged with the study, including sites that are new to cardiac DTI, making this the largest collaborative effort in the field.DiscussionSIGNET will provide important information about the key sources of variation in cardiac DTI. This will help rationalize strategies for addressing and minimizing such variation. Harmonization of protocols in this and future studies will underpin efforts to translate cardiac DTI for clinical application.

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• Journal article
  Wang Z, Xiao M, Zhou Y, Wang C, Wu N, Li Y, Gong Y, Chang S, Chen Y, Zhu L, Zhou J, Cai C, Wang H, Jiang X, Guo D, Yang G, Qu Xet al., 2025,

  Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

  , IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 72, Pages: 3642-3654, ISSN: 0018-9294
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  • Citations: 4
• Journal article
  Wang H, Chen Y, Chen W, Xu H, Zhao H, Sheng B, Fu H, Yang G, Zhu Let al., 2025,

  Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation With Selective State-Space Model.

  , IEEE Trans Med Imaging, Vol: 44, Pages: 4811-4825

  Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically spanning 200 degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies highlight that Mamba's selective State Space Model (SSM) excels in modeling long-range dependencies with linear computational complexity, making it highly suitable for preserving the continuity of elongated vessel structures, especially for high-resolution UWF images. Inspired by this, we propose the Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs. Our code will be released upon publication (https://github.com/whq-xxh/Serp-Mamba).

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• Journal article
  Jin W, Tian X, Wang N, Wu B, Shi B, Zhao B, Yang Get al., 2025,

  Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning

  , NEURAL NETWORKS, Vol: 192, ISSN: 0893-6080
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• Journal article
  Hao P, Wang H, Yang G, Zhu Let al., 2025,

  Enhancing Visual Reasoning With LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery

  , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 29, Pages: 9027-9040, ISSN: 2168-2194
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  • Citations: 2
• Journal article
  Halliday BP, Owen R, Ragavan A, Smith KL, Statton B, Berry A, Kasiakogias A, Tsoumani Z, Shanmuganathan M, Dungu JN, De Marvao A, Tayal U, Ware JS, O'regan DP, Pennell DJ, Cleland JGF, Prasad SK, Gregson J, Murphy MP, Rider OJ, Valkovic Let al., 2025,

  A double-blind, randomized placebo-controlled trial examining the effect of MitoQ on myocardial energetics in patients with dilated cardiomyopathy

  , EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, ISSN: 2047-2404
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